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LAP Lambert Academic Publishing Design of an Intelligent Control System for Conveyor-Belt Grain Dryers: An Application of Soft Computing Techniques in Grain Drying Systems

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£66.00 £62.70 £64.02 £65.34 £66.66 £67.98 £69.30 11 June 2026 03 July 2026 25 July 2026 16 August 2026 07 September 2026

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Description

The major objective of this research is to improve the performance of conveyor-belt grain dryers by designing an intelligent control system utilizing the capabilities of the adaptive neuro-fuzzy inference system (ANFIS) to model and control the drying process. To achieve this objective, a laboratory-scale conveyor-belt grain dryer was specifically fabricated for this study. As the main controller in this work, a simplified ANFIS structure is proposed to act as a proportional-integral-derivative (PID)-like feedback controller to control nonlinear systems. This controller has several advantages over its conventional ANFIS counterpart, particularly the reduction in processing time. Moreover, three evolutionary algorithms (EAs), in particular a real-coded genetic algorithm (GA), a particle swarm optimization (PSO), and a global-best harmony search (GHS), were separately used to train the proposed controller and to determine its scaling factors. The simplified ANFIS controller was then applied to control the developed ANFIS-based dryer model. From all the simulation tests, the simplified ANFIS controller has proved its remarkable ability in controlling the grain drying process.

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